Static gesture recognition is an effective non-verbal communication channel between a user and their devices; however many modern methods are sensitive to the relative pose of the user’s hands with respect to the capture device, as parts of the gesture can become occluded. We present two methodologies for gesture recognition via synchronized recording from two depth cameras to alleviate this occlusion problem. One is a more classic approach using iterative closest point registration to accurately fuse point clouds and a single PointNet architecture for classification, and the other is a dual PointNet architecture for classification without registration. On a manually collected data-set of 20,100 point clouds we show a 39.2% reduction in misclassification for the fused point cloud method, and 53.4% for the dual PointNet, when compared to a standard single camera pipeline.
Paper Details
- Authors:
- Submitted On:
- 21 September 2019 - 2:36am
- Short Link:
- Type:
- Poster
- Event:
- Presenter's Name:
- Ilya Chugunov
- Paper Code:
- 10.1109/ICIP.2019.8803665
- Document Year:
- 2019
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url = {http://sigport.org/4801},
author = {Ilya Chugunov; Avideh Zakhor },
publisher = {IEEE SigPort},
title = {DuoDepth: Static Gesture Recognition with Dual Depth Sensors},
year = {2019} }
T1 - DuoDepth: Static Gesture Recognition with Dual Depth Sensors
AU - Ilya Chugunov; Avideh Zakhor
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4801
ER -
